Cited by 16Intelligent Tutoring Systems and Adaptive LearningOnline Learning and AnalyticsEducational Technology and Assessment

Mastery Learning Heuristics and Their Hidden Models

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Doroudi, Shayan·2020·Lecture Notes in Computer Science

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  1. [1]Adams, R.P., MacKay, D.J.: Bayesian online changepoint detection. arXiv preprint arXiv:0710.3742 (2007) · arXiv
  2. [3]Aroian LA Levene H The effectiveness of quality control charts J. Am. Stat. Assoc. 1950 45 252 520 529 10.1080/01621459.1950.10501143 · DOI
  3. [5]Baker RSJD De Bra P Kobsa A Chin D Contextual Slip and prediction of student performance after use of an intelligent tutor User Modeling, Adaptation, and Personalization 2010 Heidelberg Springer 52 63
  4. [6]Beck JE Gong Y Lane HC Yacef K Mostow J Pavlik P Wheel-spinning: students who fail to master a skill Artificial Intelligence in Education 2013 Heidelberg Springer 431 440
  5. [8]Bower GH Application of a model to paired-associate learning Psychometrika 1961 26 3 255 280 10.1007/BF02289796 · DOI
  6. [9]Cen, H.: Generalized learning factors analysis: improving cognitive models with machine learning. Ph.D. thesis (2009)
  7. [11]Corbett, A.: Cognitive mastery learning in the act programming tutor. In: AAAI Tech. rep. SS-00-01 (2000)
  8. [12]Corbett AT Anderson JR Knowledge tracing: modeling the acquisition of procedural knowledge User Model. User-Adapt. Interac. 1995 4 4 253 278 10.1007/BF01099821 · DOI · Paper page
  9. [14]Karush W Dear R Optimal strategy for item presentation in a learning process Manag. Sci. 1967 13 11 773 785 10.1287/mnsc.13.11.773 · DOI
  10. [15]Kelly, K.M., Wang, Y., Thompson, T., Heffernan, N.T.: Defining mastery: knowledge tracing versus n-consecutive correct responses. In: Proceedings of the 8th International Conference on Educational Data Mining (2015)
  11. [17]Morrison HC The Practice of Teaching in the Secondary School 1926 Chicago University of Chicago Press
  12. [18]Page ES Continuous inspection schemes Biometrika 1954 41 1/2 100 115 10.2307/2333009 · DOI
  13. [19]Pelánek, R., Řihák, J.: Experimental analysis of mastery learning criteria. In: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, pp. 156–163 (2017)
  14. [20]Piech, C., et al.: Deep knowledge tracing. In: Advances in Neural Information Processing Systems, pp. 505–513 (2015)
  15. [21]Reynolds MR Jr Stoumbos ZG A CUSUM chart for monitoring a proportion when inspecting continuously J. Qual. Technol. 1999 31 1 87 108 10.1080/00224065.1999.11979900 · DOI
  16. [22]Salden RJ Aleven V Schwonke R Renkl A The expertise reversal effect and worked examples in tutored problem solving Instr. Sci. 2010 38 3 289 307 10.1007/s11251-009-9107-8 · DOI
  17. [24]Washburne CW The individual system in Winnetka Elementary Sch. J. 1920 21 1 52 68 10.1086/454876 · DOI
  18. [25]Zhang, J., Shi, X., King, I., Yeung, D.Y.: Dynamic key-value memory networks for knowledge tracing. In: Proceedings of the 26th International Conference on World Wide Web, pp. 765–774 (2017)

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Authors
Doroudi, S.
Affiliations
University of California, Irvine (US)
Corresponding author
Doroudi, Shayan
Venue
Lecture Notes in Computer Science
Published
2020
Pages
86-91
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
OpenAlex
W3040439290
Cited by
16
References
1
Citations by year
2026: 2 · 2025: 4 · 2024: 2 · 2023: 2 · 2022: 2 · 2021: 4
FWCI
0.75
Type
conference paper
Language
en
Open access
Locations
  • Lecture notes in computer science (publishedVersion)
  • PubMed Central (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
  • Physical Sciences › Computer Science › Information Systems › Educational Technology and Assessment
Keywords
learning mastery, adaptive learning, heuristics, Bayesian Knowledge Tracing, ALEKS, model-based algorithms, optimal policy, skill assessment

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